Load Data

For details about data description see here

load(file = "../output/mediatenor.Rda")

Gewichtung

\[ \text{W} = \frac{\text{Anzahl d. Beobachtungen pro Medium & Partei}}{\text{Anzahl d. Beobachtungen pro Medium}} \]

\[ \text{Gewichtete Wertung} = \text{Wertung} * \text{W} \]

Line Plots

Tageszeitungen

df.reduced %>%
  filter(category == "daily_print") %>%
  ggplot(aes(year, count, color=medium)) +
  geom_line() +
  facet_wrap(~p_group) +
  labs(x="", y="", color="",
       title="Tageszeitungen: Anzahl d. Beobachtungen")

p <- df.reduced %>%
  filter(category == "daily_print")

ggplot(p, aes(year, weighted, color=p_group, 
             group=p_group)) +
  geom_point(size=p$count/10000) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", 
       title="Tageszeitungen: Wertung (gewichtet)", 
       subtitle = "Pointsize: Obs/10.000",
       color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 
## Warning: Removed 31 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_path).

Magazine und Wochenzeitungen

df.reduced %>%
  filter(category == "magazine_print") %>%
  ggplot(aes(year, count, color=medium)) +
  geom_line() +
  facet_wrap(~p_group) +
  labs(x="", y="", color="",
       title="Magazine und Wochenzeitungen: Anzahl d. Beobachtungen")

p <- df.reduced %>%
  filter(category == "magazine_print")

ggplot(p, aes(year, weighted, color=p_group, group = p_group)) +
  geom_point(size=p$count/10000) + geom_line() +
  facet_wrap(~medium, ncol = 5) +
    geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", color="",
       title="Magazine und Wochenzeitungen: Wertung (gewichtet)", 
       subtitle = "Pointsize: Obs/10.000")  +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 
## Warning: Removed 13 rows containing missing values (geom_point).

Nachrichtensendungen

df.reduced %>%
  filter(category == "news_tv") %>%
  ggplot(aes(year, count, color=medium)) +
  geom_line() +
  facet_wrap(~p_group) +
  labs(x="", y="", color="",
       title="Nachrichtensendungen: Anzahl d. Beobachtungen")

p <- df.reduced %>%
  filter(category == "news_tv")

ggplot(p, aes(year, weighted, color=p_group)) +
  geom_point(size=p$count/10000) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", 
       title="Nachrichtensendungen (gewichtet)",
       subtitle = "Pointsize: Obs/10.000",
       color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 
## Warning: Removed 4 rows containing missing values (geom_point).

Politische TV-Shows

df.reduced %>%
  filter(category == "polit_tv") %>%
  ggplot(aes(year, count, color=medium)) +
  geom_line() +
  facet_wrap(~p_group) +
  labs(x="", y="", color="",
       title="Politische TV-Shows: Anzahl d. Beobachtungen")

p <- df.reduced %>%
  filter(category == "polit_tv")

ggplot(p, aes(year, weighted, color=p_group)) +
  geom_point(size=p$count/1000) + geom_line() +
  facet_wrap(~medium, ncol = 6) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", 
       title="Politische TV-Shows (gewichtet)", 
       substitle = "Pointsize: Obs/ 1000",
       color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))

Radarcharts

require(ggiraph)
require(ggiraphExtra)

Tageszeitungen

radar <- df.reduced %>% 
  filter(category == "daily_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T),
                   count = mean(count, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  select(- count) %>%
  ggRadar(aes(color=medium), rescale = F,
          size=radar$count/10000,
          alpha = 0, legend.position = "right") +
  labs(title = "Tageszeitungen:\nWertung (gewichtet) 1998-2012",
       subtitle = "Pointsize = Obs / 10.000")

Magazine und Wochenzeitungen

radar <- df.reduced %>% 
  filter(category == "magazine_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T),
                   count = mean(count, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  select(- count) %>%
  ggRadar(aes(color=medium), rescale = F, 
          size=radar$count/1000,
          alpha = 0, legend.position = "right") +
  labs(title = "Magazine und Wochenzeitungen:\nWertung (gewichtet) 1998 - 2012",
       subtitle = "Pointsize = Obs / 1.000")

Nachritensendungen

radar <- df.reduced %>% 
  filter(category == "news_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T),
                   count = mean(count, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  select(- count) %>%
  ggRadar(aes(color=medium), rescale = F,
                    size=radar$count/1000,
          alpha = 0, legend.position = "right") +
  labs(title = "Nachritensendungen: Wertung (gewichtet)",
       subtitle = "Pointsize = Obs / 1.000")

Pro Jahr

Politische TV-Shows

radar <- df.reduced %>% 
  filter(category == "polit_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T),
                   count = mean(count, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  select(- count) %>%
  ggRadar(aes(color=medium), rescale = F,
          size=radar$count/500,
          alpha = 0, legend.position = "right") +
  labs(title = "Politische TV-Shows: Wertung (gewichtet)",
       subtitle = "Pointsize = Obs / 500")

Pro Jahr